class_docstr: Class for complement graphs.
  
  The main goal is to be able to work with big and dense graphs with
  a low memory footprint.
  
  In this class you add the edges that *do not exist* in the dense graph,
  the report methods of the class return the neighbors, the edges and
  the degree as if it was the dense graph. Thus it's possible to use
  an instance of this class with some of NetworkX functions.
docstr: Return an iterator for (node, degree) in the dense graph.
  
  The node degree is the number of edges adjacent to the node.
  
  Parameters
  ----------
  nbunch : iterable container, optional (default=all nodes)
      A container of nodes.  The container will be iterated
      through once.
  
  weight : string or None, optional (default=None)
     The edge attribute that holds the numerical value used
     as a weight.  If None, then each edge has weight 1.
     The degree is the sum of the edge weights adjacent to the node.
  
  Returns
  -------
  nd_iter : iterator
      The iterator returns two-tuples of (node, degree).
  
  See Also
  --------
  degree
  
  Examples
  --------
  >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
  >>> G.degree(0)  # node 0 with degree 1
  1
  >>> list(G.degree([0, 1]))
  [(0, 1), (1, 2)]
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code: def degree(self, nbunch=None, weight=None):
      """Return an iterator for (node, degree) in the dense graph.
  
      The node degree is the number of edges adjacent to the node.
  
      Parameters
      ----------
      nbunch : iterable container, optional (default=all nodes)
          A container of nodes.  The container will be iterated
          through once.
  
      weight : string or None, optional (default=None)
         The edge attribute that holds the numerical value used
         as a weight.  If None, then each edge has weight 1.
         The degree is the sum of the edge weights adjacent to the node.
  
      Returns
      -------
      nd_iter : iterator
          The iterator returns two-tuples of (node, degree).
  
      See Also
      --------
      degree
  
      Examples
      --------
      >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
      >>> G.degree(0)  # node 0 with degree 1
      1
      >>> list(G.degree([0, 1]))
      [(0, 1), (1, 2)]
  
      """
      if nbunch is None:
          nodes_nbrs = (
              (
                  n,
                  {
                      v: self.all_edge_dict
                      for v in set(self.adj) - set(self.adj[n]) - {n}
                  },
              )
              for n in self.nodes()
          )
      elif nbunch in self:
          nbrs = set(self.nodes()) - set(self.adj[nbunch]) - {nbunch}
          return len(nbrs)
      else:
          nodes_nbrs = (
              (
                  n,
                  {
                      v: self.all_edge_dict
                      for v in set(self.nodes())
                      - set(self.adj[n])
                      - {n}
                  },
              )
              for n in self.nbunch_iter(nbunch)
          )
  
      if weight is None:
          return ((n, len(nbrs)) for n, nbrs in nodes_nbrs)
      else:
          # AntiGraph is a ThinGraph so all edges have weight 1
          return (
              (n, sum((nbrs[nbr].get(weight, 1)) for nbr in nbrs))
              for n, nbrs in nodes_nbrs
          )
  
file_path: networkx/examples/subclass/plot_antigraph.py
imports: import matplotlib.pyplot
  import networkx
  from networkx import Graph
label_desc: Use image node_id 4 for calling the AntiGraph obj's underlying member method code with example usage: obj.degree(nbunch, weight) and returns: unknown, unknown, len